diff --git a/examples/User_and_product_embeddings.ipynb b/examples/User_and_product_embeddings.ipynb
index 565ccd2890..877775f797 100644
--- a/examples/User_and_product_embeddings.ipynb
+++ b/examples/User_and_product_embeddings.ipynb
@@ -1,13 +1,15 @@
 {
  "cells": [
   {
-   "cell_type": "markdown",
-   "metadata": {},
+   "cell_type": "raw",
    "source": [
     "## User and product embeddings\n",
     "\n",
-    "We calculate user and product embeddings based on the training set, and evaluate the results on the unseen test set. We will evaluate the results by plotting the user and product similarity versus the review score. The dataset is created in the [Obtain_dataset Notebook](Obtain_dataset.ipynb)."
-   ]
+    "We calculate user and product embeddings based on the training set, and evaluate the results on the unseen test set. We will evaluate the results by plotting the user and product similarity versus the review score."
+   ],
+   "metadata": {
+    "collapsed": false
+   }
   },
   {
    "cell_type": "markdown",
@@ -21,13 +23,16 @@
   {
    "cell_type": "code",
    "execution_count": 2,
-   "metadata": {},
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2023-06-21T23:42:37.817424499Z",
+     "start_time": "2023-06-21T23:42:35.111594233Z"
+    }
+   },
    "outputs": [
     {
      "data": {
-      "text/plain": [
-       "(24502, 19035)"
-      ]
+      "text/plain": "(577, 706)"
      },
      "execution_count": 2,
      "metadata": {},
@@ -39,12 +44,12 @@
     "import numpy as np\n",
     "from sklearn.model_selection import train_test_split\n",
     "\n",
-    "df = pd.read_csv('output/embedded_babbage_similarity_50k.csv', index_col=0)  # note that you will need to generate this file to run the code below\n",
-    "df['babbage_similarity'] = df.babbage_similarity.apply(eval).apply(np.array)\n",
+    "df = pd.read_csv('data/fine_food_reviews_with_embeddings_1k.csv', index_col=0)\n",
+    "df['embedding'] = df.embedding.apply(eval).apply(np.array)\n",
     "X_train, X_test, y_train, y_test = train_test_split(df, df.Score, test_size = 0.2, random_state=42)\n",
     "\n",
-    "user_embeddings = X_train.groupby('UserId').babbage_similarity.apply(np.mean)\n",
-    "prod_embeddings = X_train.groupby('ProductId').babbage_similarity.apply(np.mean)\n",
+    "user_embeddings = X_train.groupby('UserId').embedding.apply(np.mean)\n",
+    "prod_embeddings = X_train.groupby('ProductId').embedding.apply(np.mean)\n",
     "len(user_embeddings), len(prod_embeddings)\n"
    ]
   },
@@ -67,7 +72,12 @@
   {
    "cell_type": "code",
    "execution_count": 3,
-   "metadata": {},
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2023-06-21T23:42:38.241991598Z",
+     "start_time": "2023-06-21T23:42:37.819696246Z"
+    }
+   },
    "outputs": [],
    "source": [
     "from openai.embeddings_utils import cosine_similarity\n",
@@ -99,26 +109,27 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {},
+   "execution_count": 4,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2023-06-21T23:42:38.452315248Z",
+     "start_time": "2023-06-21T23:42:38.243944540Z"
+    }
+   },
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Correlation between user&vector similarity percentile metric and review number of stars (score): 22.11%\n"
+      "Correlation between user & vector similarity percentile metric and review number of stars (score): 17.25%\n"
      ]
     },
     {
      "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
+      "text/plain": "<Figure size 640x480 with 1 Axes>",
+      "image/png": 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"
      },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -152,8 +163,9 @@
    "hash": "be4b5d5b73a21c599de40d6deb1129796d12dc1cc33a738f7bac13269cfcafe8"
   },
   "kernelspec": {
-   "display_name": "Python 3.7.3 64-bit ('base': conda)",
-   "name": "python3"
+   "name": "python3",
+   "language": "python",
+   "display_name": "Python 3 (ipykernel)"
   },
   "language_info": {
    "codemirror_mode": {